# import a library for openning URL
import urllib.request

# prepare URL to be opened
filename = 'arbys.csv' # 'wendys.csv' #'arbys.csv'
url = 'http://cs1110.cs.virginia.edu/files/' + filename

# let's look at the URL
print("url = " + url)

# open the specified URL to read
# store the response object received from the server in a variable
stream = urllib.request.urlopen(url)
# print(stream)   # http.client.HTTPResponse object


# Tips:
# - Instead of "open()" when opening a file,
#   we use "urlopen()" when opening a url
# - Once opening a url, we must decode the data
#   -- unlike reading the file, we can simply read() or readline()


print("\n==== Start processing data ==== \n")


list_of_store = []

# # read each line from the response object
for line in stream:

    # decode the string using the standard encoding scheme (UTF-8)
    # then, strip leading and tailing spaces, and split the string using ","
    # decoded_data = line.decode("UTF-8").strip()
    # print(decoded_data)

    store_location = line.decode("UTF-8").strip().split(",")   # split results in list
    list_of_store.append(store_location)

stores_in_state = {}

# Count the number of stores in each state.
# Then print a dictionary containing pairs
# of states and number of store, where
#    key is the state and
#    value is the number store in the state
# For example,
# {'VA': 106, 'AR': 43, 'NS': 2, 'IN': 175, 'PA': 137, 'MO': 85, 'SC': 69, 'ID': 19, 'MN': 81, 'WV': 39, 'KS': 55, 'ND': 11, 'TN': 106, 'NH': 2, 'NM': 27, 'AZ': 70, 'CT': 8, 'FL': 140, 'NJ': 19, 'SK': 4, 'TX': 132, 'AK': 8, 'IL': 125, 'AB': 27, 'MB': 2, 'HI': 3, 'LA': 27, 'MA': 4, 'NY': 73, 'NB': 2, 'SD': 19, 'MI': 173, 'KY': 117, 'OK': 91, 'CA': 98, 'WI': 90, 'WY': 16, 'CO': 61, 'WA': 55, 'BC': 8, 'MD': 43, 'MS': 23, 'OH': 268, 'NE': 44, 'IA': 54, 'OR': 29, 'ON': 29, 'UT': 66, 'AL': 97, 'MT': 19, 'NC': 130, 'ME': 5, 'NV': 26, 'GA': 143, 'DE': 15}


for store_info in list_of_store:
    # print(store_info)

    # keep track how many stores are in the same state
    state = store_info[3].strip()
    if state in stores_in_state:
        stores_in_state[state] += 1
    else:
        stores_in_state[state] = 1

print(stores_in_state)





# What can we do with this dataset?
#  - Count how many days the weather was clear
#  - Compute the average temperature of the day
#  - Find patterns and estimate likelyhood